Centre for Academic Primary Care, Bristol.
Department of Primary Care and Population Health, University College London, London.
Br J Gen Pract. 2018 Mar;68(668):e146-e156. doi: 10.3399/bjgp18X694829. Epub 2018 Jan 29.
The National Institute for Health and Care Excellence (NICE) recommends that GPs use routinely available data to identify patients most at risk of death and ill health from living in cold homes.
To investigate whether sociodemographic characteristics, clinical factors, and house energy efficiency characteristics could predict cold-related mortality.
A case-crossover analysis was conducted on 34 777 patients aged ≥65 years from the Clinical Practice Research Datalink who died between April 2012 and March 2014. The average temperature of date of death and 3 days previously were calculated from Met Office data. The average 3-day temperature for the 28th day before/after date of death were calculated, and comparisons were made between these temperatures and those experienced around the date of death.
Conditional logistic regression was applied to estimate the odds ratio (OR) of death associated with temperature and interactions between temperature and sociodemographic characteristics, clinical factors, and house energy efficiency characteristics, expressed as relative odds ratios (RORs).
Lower 3-day temperature was associated with higher risk of death (OR 1.011 per 1°C fall; 95% CI = 1.007 to 1.015; <0.001). No modifying effects were observed for sociodemographic characteristics, clinical factors, and house energy efficiency characteristics. Analysis of winter deaths for causes typically associated with excess winter mortality ( = 7710) showed some evidence of a weaker effect of lower 3-day temperature for females (ROR 0.980 per 1°C, 95% CI = 0.959 to 1.002, = 0.082), and a stronger effect for patients living in northern England (ROR 1.040 per 1°C, 95% CI = 1.013 to 1.066, = 0.002).
It is unlikely that GPs can identify older patients at highest risk of cold-related death using routinely available data, and NICE may need to refine its guidance.
英国国家卫生与临床优化研究所（NICE）建议全科医生常规使用现有数据来识别那些因居住在寒冷家中而面临更高死亡和健康不良风险的患者。
研究社会人口统计学特征、临床因素和房屋能源效率特征是否可用于预测与寒冷相关的死亡。
对临床实践研究数据链中年龄≥65 岁的 34777 名患者进行病例交叉分析，这些患者在 2012 年 4 月至 2014 年 3 月之间死亡。从英国气象局的数据中计算出死亡日期及前 3 天的平均温度。计算出死亡日期前/后第 28 天的 3 天平均温度，并将这些温度与死亡日期前后的温度进行比较。
应用条件逻辑回归来估计与温度相关的死亡风险的比值比（OR），并对温度与社会人口统计学特征、临床因素和房屋能源效率特征之间的相互作用进行检验，结果表示为相对比值比（ROR）。
3 天的平均温度每下降 1°C，死亡风险就会增加 1.011（95%CI = 1.007 至 1.015；<0.001）。社会人口统计学特征、临床因素和房屋能源效率特征没有观察到调节作用。对通常与冬季超额死亡相关的死因进行冬季死亡分析（n = 7710），结果显示对于女性，低温对 3 天平均温度的影响较弱（ROR 每降低 1°C 为 0.980，95%CI = 0.959 至 1.002， = 0.082），而对于居住在英格兰北部的患者，这种影响更强（ROR 每降低 1°C 为 1.040，95%CI = 1.013 至 1.066， = 0.002）。
全科医生不太可能使用常规可用数据识别出与寒冷相关死亡风险最高的老年患者，NICE 可能需要对其指南进行修订。
